Complex Feedback Strategies for Hypothesis Generation and Verification

We extend the idea of the single-pass feedback framework by employing complex feedback strategies for both more robust hypothesis generation and hypothesis veri cation. These strategies are developed at every level of our object recognition application; from low-level parameter optimisation, through the low level processing chain, to higher level recognition stages. The strategies are independent of the techniques used at each level. We introduce various control mechanisms to achieve such complex feedback strategies. Within our implementation, we minimise the amount of feedback to false alarms by using an interest operator which directs the search through the hypotheses in an optimal manner. Furthermore, we obtain detailed information about a complex object and not just its location. Thus, following top-down recognition of the object our feedback control directs the search for missing information. We illustrate our approach using noisy infra-red images of bridges in real outdoor scenes and demonstrate the insensitivity of the system to noisy data and partial occlusion.